Diagnosing Language Transfer in a Webbased ICALL that SelfImproves its Student Modeler

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Diagnosing Language Transfer in a Web-based ICALL
that Self-Improves its Student Modeler

• Victoria Tsiriga Maria Virvou
• Department of Informatics,
• University of Piraeus,
• 80 Karaoli Dimitriou St.,
• Piraeus 18534, Greece,
• vtsir_at_unipi.gr, mvirvou_at_unipi.gr

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Adaptivity in Web-based Tutoring Systems

• Adaptivity is crucial in Web-based tutoring
  systems.
• To be adaptive, a Web-based educational system
  should be able to draw inferences about
  individual students.
• Therefore, the student modelling component is
  crucial for the purpose of adaptation.

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Adaptivity in Web-based Intelligent Computer
Assisted Language Learning (ICALL) Systems

• In Web-based ICALL systems, the students prior
  knowledge of other languages is important.
• Language transfer is the interference resulting
  from the similarities and differences between the
  target language and other languages the student
  knows.
• According to some, Web-based system must adopt a
  more general scheme in order to accommodate the
  international nature of the Internet.
• Using a machine learning mechanism would allow an
  ICALL system to learn how a language may
  interfere in learning the target language.

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Overview of Web-Passive Voice Tutor (Web-PVT)

• Web-PVT is an adaptive and intelligent Web-based
  tutoring system that aims at teaching non-native
  speakers the domain of the passive voice of the
  English language.
• Web-PVT incorporates techniques from Intelligent
  Tutoring Systems and Adaptive Hypermedia to
  tailor instruction and feedback to each
  individual student.

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Error Categories
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Explanation about the cause of a mistake

• Language Transfer.
• Overgeneralization of the target language rules.
• Ignorance of rule restrictions.
• Incomplete application of rules.
• False concepts hypothesized.
• Carelessness.

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Categories of Error and Language Transfer

• Language transfer may cause many mistakes.
• Associating categories of error with language
  transfer would require eliciting the expertise of
  many human experts.
• In Web-PVT, the association of the categories of
  error with language transfer is performed
  dynamically.

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Acquiring Initial Information about the Student
in Web-PVT

• Direct Provision by the student
• name,
• mother tongue,
• other languages s/he already knows, and
• self-categorization concerning how careful s/he
  is when solving exercises.
• A preliminary test to assess the knowledge level
  of the student in the domain.

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Representation of the Student Characteristics

• The information acquired by the student in
  her/his first interaction with Web-PVT is
  represented in a feature vector
• ltStudent_Code, Name, Knowledge_Level,
  Carefulness, Mother_Tongue, Language1, Language2,
  gt

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Distance Weighted k-Nearest Neighbor Algorithm

• It is used to estimate the students proneness to
  make each category of error.
• This is done using information about other
  students of the same knowledge level category,
  who speak the same languages as the new student.
• The contribution of each neighbor is weighted
  based on her/his distance from the new student.

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Calculating Distance between Students
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Calculating Distance between Students
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Defining k in the k-Nearest Neighbor Algorithm

• In Web-PVT the number of k is defined to be the
  number of students that belong to the same
  knowledge level category with the new student.
• Students that belong to different knowledge level
  categories are not expected to have similar
  knowledge of the domain, irrespective of their
  other personal characteristics.

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Estimating Error Proneness
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Main Points

• Language transfer is important for ICALL systems
  due to the fact that students often use already
  acquired knowledge while learning a new subject.
• Our approach to student modeling is based on
  recognized similarities of the new student with
  other students that have already interacted with
  the system.